Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations9357
Missing cells13554
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory156.2 B

Variable types

DateTime1
Categorical1
Text5
Numeric8

Alerts

NMHC(GT) is highly overall correlated with NO2(GT) and 6 other fieldsHigh correlation
NO2(GT) is highly overall correlated with NMHC(GT) and 5 other fieldsHigh correlation
NOx(GT) is highly overall correlated with NMHC(GT) and 5 other fieldsHigh correlation
PT08.S1(CO) is highly overall correlated with NMHC(GT) and 6 other fieldsHigh correlation
PT08.S2(NMHC) is highly overall correlated with NMHC(GT) and 6 other fieldsHigh correlation
PT08.S3(NOx) is highly overall correlated with NMHC(GT) and 6 other fieldsHigh correlation
PT08.S4(NO2) is highly overall correlated with NMHC(GT) and 4 other fieldsHigh correlation
PT08.S5(O3) is highly overall correlated with NMHC(GT) and 6 other fieldsHigh correlation
PT08.S1(CO) has 366 (3.9%) missing valuesMissing
NMHC(GT) has 8443 (90.2%) missing valuesMissing
PT08.S2(NMHC) has 366 (3.9%) missing valuesMissing
NOx(GT) has 1639 (17.5%) missing valuesMissing
PT08.S3(NOx) has 366 (3.9%) missing valuesMissing
NO2(GT) has 1642 (17.5%) missing valuesMissing
PT08.S4(NO2) has 366 (3.9%) missing valuesMissing
PT08.S5(O3) has 366 (3.9%) missing valuesMissing
Time is uniformly distributedUniform

Reproduction

Analysis started2025-11-22 09:04:16.342980
Analysis finished2025-11-22 09:04:47.435716
Duration31.09 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Date
Date

Distinct391
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size404.2 KiB
Minimum2004-01-04 00:00:00
Maximum2005-12-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-22T10:04:47.654954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:48.092805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Categorical

Uniform 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size404.2 KiB
18.00.00
 
390
19.00.00
 
390
20.00.00
 
390
21.00.00
 
390
22.00.00
 
390
Other values (19)
7407 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters74856
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18.00.00
2nd row19.00.00
3rd row20.00.00
4th row21.00.00
5th row22.00.00

Common Values

ValueCountFrequency (%)
18.00.00390
 
4.2%
19.00.00390
 
4.2%
20.00.00390
 
4.2%
21.00.00390
 
4.2%
22.00.00390
 
4.2%
23.00.00390
 
4.2%
00.00.00390
 
4.2%
01.00.00390
 
4.2%
02.00.00390
 
4.2%
03.00.00390
 
4.2%
Other values (14)5457
58.3%

Length

2025-11-22T10:04:48.418039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18.00.00390
 
4.2%
19.00.00390
 
4.2%
20.00.00390
 
4.2%
21.00.00390
 
4.2%
22.00.00390
 
4.2%
23.00.00390
 
4.2%
00.00.00390
 
4.2%
01.00.00390
 
4.2%
02.00.00390
 
4.2%
03.00.00390
 
4.2%
Other values (14)5457
58.3%

Most occurring characters

ValueCountFrequency (%)
042498
56.8%
.18714
25.0%
15067
 
6.8%
22730
 
3.6%
31170
 
1.6%
8780
 
1.0%
9780
 
1.0%
4780
 
1.0%
5779
 
1.0%
6779
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)74856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
042498
56.8%
.18714
25.0%
15067
 
6.8%
22730
 
3.6%
31170
 
1.6%
8780
 
1.0%
9780
 
1.0%
4780
 
1.0%
5779
 
1.0%
6779
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)74856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
042498
56.8%
.18714
25.0%
15067
 
6.8%
22730
 
3.6%
31170
 
1.6%
8780
 
1.0%
9780
 
1.0%
4780
 
1.0%
5779
 
1.0%
6779
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)74856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
042498
56.8%
.18714
25.0%
15067
 
6.8%
22730
 
3.6%
31170
 
1.6%
8780
 
1.0%
9780
 
1.0%
4780
 
1.0%
5779
 
1.0%
6779
 
1.0%

CO(GT)
Text

Distinct104
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size404.2 KiB
2025-11-22T10:04:49.184233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0833601
Min length1

Characters and Unicode

Total characters28851
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row2,6
2nd row2
3rd row2,2
4th row2,2
5th row1,6
ValueCountFrequency (%)
2001592
 
17.0%
1,4279
 
3.0%
1,6275
 
2.9%
1,5273
 
2.9%
1,1262
 
2.8%
0,7260
 
2.8%
1,7258
 
2.8%
1,3253
 
2.7%
0,8251
 
2.7%
0,9248
 
2.7%
Other values (94)5406
57.8%
2025-11-22T10:04:51.092930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,7220
25.0%
05240
18.2%
24096
14.2%
13316
11.5%
31685
 
5.8%
-1683
 
5.8%
41315
 
4.6%
51044
 
3.6%
6959
 
3.3%
7823
 
2.9%
Other values (2)1470
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)28851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,7220
25.0%
05240
18.2%
24096
14.2%
13316
11.5%
31685
 
5.8%
-1683
 
5.8%
41315
 
4.6%
51044
 
3.6%
6959
 
3.3%
7823
 
2.9%
Other values (2)1470
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,7220
25.0%
05240
18.2%
24096
14.2%
13316
11.5%
31685
 
5.8%
-1683
 
5.8%
41315
 
4.6%
51044
 
3.6%
6959
 
3.3%
7823
 
2.9%
Other values (2)1470
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,7220
25.0%
05240
18.2%
24096
14.2%
13316
11.5%
31685
 
5.8%
-1683
 
5.8%
41315
 
4.6%
51044
 
3.6%
6959
 
3.3%
7823
 
2.9%
Other values (2)1470
 
5.1%

PT08.S1(CO)
Real number (ℝ)

High correlation  Missing 

Distinct1041
Distinct (%)11.6%
Missing366
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean1099.8332
Minimum647
Maximum2040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.2 KiB
2025-11-22T10:04:51.500771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum647
5-th percentile810.5
Q1937
median1063
Q31231
95-th percentile1508
Maximum2040
Range1393
Interquartile range (IQR)294

Descriptive statistics

Standard deviation217.08004
Coefficient of variation (CV)0.19737542
Kurtosis0.33512865
Mean1099.8332
Median Absolute Deviation (MAD)142
Skewness0.75590737
Sum9888600
Variance47123.743
MonotonicityNot monotonic
2025-11-22T10:04:51.988399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97330
 
0.3%
110028
 
0.3%
92526
 
0.3%
96926
 
0.3%
98826
 
0.3%
93826
 
0.3%
96625
 
0.3%
98725
 
0.3%
105325
 
0.3%
98425
 
0.3%
Other values (1031)8729
93.3%
(Missing)366
 
3.9%
ValueCountFrequency (%)
6471
 
< 0.1%
6491
 
< 0.1%
6551
 
< 0.1%
6673
< 0.1%
6691
 
< 0.1%
6761
 
< 0.1%
6781
 
< 0.1%
6791
 
< 0.1%
6811
 
< 0.1%
6832
< 0.1%
ValueCountFrequency (%)
20401
< 0.1%
20081
< 0.1%
19821
< 0.1%
19751
< 0.1%
19731
< 0.1%
19611
< 0.1%
19561
< 0.1%
19341
< 0.1%
19181
< 0.1%
19171
< 0.1%

NMHC(GT)
Real number (ℝ)

High correlation  Missing 

Distinct429
Distinct (%)46.9%
Missing8443
Missing (%)90.2%
Infinite0
Infinite (%)0.0%
Mean218.81182
Minimum7
Maximum1189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.2 KiB
2025-11-22T10:04:52.425963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile28.65
Q167
median150
Q3297
95-th percentile661.4
Maximum1189
Range1182
Interquartile range (IQR)230

Descriptive statistics

Standard deviation204.45992
Coefficient of variation (CV)0.93440987
Kurtosis2.270289
Mean218.81182
Median Absolute Deviation (MAD)94
Skewness1.5570171
Sum199994
Variance41803.859
MonotonicityNot monotonic
2025-11-22T10:04:52.867902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6614
 
0.1%
409
 
0.1%
299
 
0.1%
888
 
0.1%
938
 
0.1%
957
 
0.1%
847
 
0.1%
577
 
0.1%
557
 
0.1%
607
 
0.1%
Other values (419)831
 
8.9%
(Missing)8443
90.2%
ValueCountFrequency (%)
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
142
< 0.1%
161
 
< 0.1%
174
< 0.1%
182
< 0.1%
192
< 0.1%
ValueCountFrequency (%)
11891
< 0.1%
11291
< 0.1%
10841
< 0.1%
10421
< 0.1%
9741
< 0.1%
9261
< 0.1%
8991
< 0.1%
8801
< 0.1%
8721
< 0.1%
8401
< 0.1%
Distinct408
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size404.2 KiB
2025-11-22T10:04:54.039267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.5084963
Min length3

Characters and Unicode

Total characters32829
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)0.7%

Sample

1st row11,9
2nd row9,4
3rd row9,0
4th row9,2
5th row6,5
ValueCountFrequency (%)
200,0366
 
3.9%
3,684
 
0.9%
2,882
 
0.9%
3,879
 
0.8%
4,078
 
0.8%
3,177
 
0.8%
3,076
 
0.8%
2,575
 
0.8%
2,973
 
0.8%
5,472
 
0.8%
Other values (398)8295
88.7%
2025-11-22T10:04:55.303527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,9357
28.5%
14513
13.7%
23117
 
9.5%
02823
 
8.6%
32159
 
6.6%
41933
 
5.9%
51874
 
5.7%
61830
 
5.6%
71700
 
5.2%
81626
 
5.0%
Other values (2)1897
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)32829
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,9357
28.5%
14513
13.7%
23117
 
9.5%
02823
 
8.6%
32159
 
6.6%
41933
 
5.9%
51874
 
5.7%
61830
 
5.6%
71700
 
5.2%
81626
 
5.0%
Other values (2)1897
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32829
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,9357
28.5%
14513
13.7%
23117
 
9.5%
02823
 
8.6%
32159
 
6.6%
41933
 
5.9%
51874
 
5.7%
61830
 
5.6%
71700
 
5.2%
81626
 
5.0%
Other values (2)1897
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32829
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,9357
28.5%
14513
13.7%
23117
 
9.5%
02823
 
8.6%
32159
 
6.6%
41933
 
5.9%
51874
 
5.7%
61830
 
5.6%
71700
 
5.2%
81626
 
5.0%
Other values (2)1897
 
5.8%

PT08.S2(NMHC)
Real number (ℝ)

High correlation  Missing 

Distinct1245
Distinct (%)13.8%
Missing366
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean939.15338
Minimum383
Maximum2214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.2 KiB
2025-11-22T10:04:55.621706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum383
5-th percentile562
Q1734.5
median909
Q31116
95-th percentile1420
Maximum2214
Range1831
Interquartile range (IQR)381.5

Descriptive statistics

Standard deviation266.83143
Coefficient of variation (CV)0.28411912
Kurtosis0.063243873
Mean939.15338
Median Absolute Deviation (MAD)188
Skewness0.56156598
Sum8443928
Variance71199.011
MonotonicityNot monotonic
2025-11-22T10:04:55.981477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85325
 
0.3%
85923
 
0.2%
80023
 
0.2%
88023
 
0.2%
98522
 
0.2%
85021
 
0.2%
78321
 
0.2%
77621
 
0.2%
76921
 
0.2%
96220
 
0.2%
Other values (1235)8771
93.7%
(Missing)366
 
3.9%
ValueCountFrequency (%)
3832
< 0.1%
3871
< 0.1%
3881
< 0.1%
3902
< 0.1%
3971
< 0.1%
3991
< 0.1%
4022
< 0.1%
4072
< 0.1%
4081
< 0.1%
4091
< 0.1%
ValueCountFrequency (%)
22141
< 0.1%
20071
< 0.1%
19831
< 0.1%
19811
< 0.1%
19801
< 0.1%
19591
< 0.1%
19581
< 0.1%
19351
< 0.1%
19241
< 0.1%
19201
< 0.1%

NOx(GT)
Real number (ℝ)

High correlation  Missing 

Distinct925
Distinct (%)12.0%
Missing1639
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean246.89673
Minimum2
Maximum1479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.2 KiB
2025-11-22T10:04:56.315075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile38
Q198
median180
Q3326
95-th percentile693
Maximum1479
Range1477
Interquartile range (IQR)228

Descriptive statistics

Standard deviation212.97917
Coefficient of variation (CV)0.86262448
Kurtosis3.4021344
Mean246.89673
Median Absolute Deviation (MAD)100
Skewness1.7157808
Sum1905549
Variance45360.126
MonotonicityNot monotonic
2025-11-22T10:04:56.636453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8941
 
0.4%
6537
 
0.4%
4136
 
0.4%
12236
 
0.4%
9336
 
0.4%
13235
 
0.4%
9535
 
0.4%
18035
 
0.4%
5134
 
0.4%
12034
 
0.4%
Other values (915)7359
78.6%
(Missing)1639
 
17.5%
ValueCountFrequency (%)
21
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
103
< 0.1%
114
< 0.1%
124
< 0.1%
134
< 0.1%
ValueCountFrequency (%)
14791
< 0.1%
13892
< 0.1%
13691
< 0.1%
13581
< 0.1%
13451
< 0.1%
13101
< 0.1%
13011
< 0.1%
12901
< 0.1%
12531
< 0.1%
12471
< 0.1%

PT08.S3(NOx)
Real number (ℝ)

High correlation  Missing 

Distinct1221
Distinct (%)13.6%
Missing366
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean835.4936
Minimum322
Maximum2683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.2 KiB
2025-11-22T10:04:56.982254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum322
5-th percentile483
Q1658
median806
Q3969.5
95-th percentile1291
Maximum2683
Range2361
Interquartile range (IQR)311.5

Descriptive statistics

Standard deviation256.81732
Coefficient of variation (CV)0.30738394
Kurtosis2.6775589
Mean835.4936
Median Absolute Deviation (MAD)155
Skewness1.1017292
Sum7511923
Variance65955.136
MonotonicityNot monotonic
2025-11-22T10:04:57.330684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76725
 
0.3%
84625
 
0.3%
73325
 
0.3%
87623
 
0.2%
76523
 
0.2%
83022
 
0.2%
68522
 
0.2%
87222
 
0.2%
89122
 
0.2%
84522
 
0.2%
Other values (1211)8760
93.6%
(Missing)366
 
3.9%
ValueCountFrequency (%)
3221
< 0.1%
3252
< 0.1%
3281
< 0.1%
3302
< 0.1%
3341
< 0.1%
3351
< 0.1%
3402
< 0.1%
3411
< 0.1%
3451
< 0.1%
3461
< 0.1%
ValueCountFrequency (%)
26831
< 0.1%
25591
< 0.1%
25421
< 0.1%
23311
< 0.1%
23271
< 0.1%
23181
< 0.1%
22941
< 0.1%
21211
< 0.1%
20952
< 0.1%
20811
< 0.1%

NO2(GT)
Real number (ℝ)

High correlation  Missing 

Distinct283
Distinct (%)3.7%
Missing1642
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean113.09125
Minimum2
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.2 KiB
2025-11-22T10:04:57.682631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile43
Q178
median109
Q3142
95-th percentile200.3
Maximum340
Range338
Interquartile range (IQR)64

Descriptive statistics

Standard deviation48.370108
Coefficient of variation (CV)0.42770866
Kurtosis0.46503212
Mean113.09125
Median Absolute Deviation (MAD)32
Skewness0.62171431
Sum872499
Variance2339.6673
MonotonicityNot monotonic
2025-11-22T10:04:58.099492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9778
 
0.8%
11777
 
0.8%
11977
 
0.8%
9575
 
0.8%
10175
 
0.8%
11475
 
0.8%
11074
 
0.8%
11573
 
0.8%
10772
 
0.8%
11672
 
0.8%
Other values (273)6967
74.5%
(Missing)1642
 
17.5%
ValueCountFrequency (%)
21
 
< 0.1%
31
 
< 0.1%
52
 
< 0.1%
71
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
112
 
< 0.1%
122
 
< 0.1%
131
 
< 0.1%
145
0.1%
ValueCountFrequency (%)
3401
< 0.1%
3331
< 0.1%
3261
< 0.1%
3221
< 0.1%
3121
< 0.1%
3101
< 0.1%
3091
< 0.1%
3061
< 0.1%
3011
< 0.1%
2961
< 0.1%

PT08.S4(NO2)
Real number (ℝ)

High correlation  Missing 

Distinct1603
Distinct (%)17.8%
Missing366
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean1456.2646
Minimum551
Maximum2775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.2 KiB
2025-11-22T10:04:58.469151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum551
5-th percentile883
Q11227
median1463
Q31674
95-th percentile2029
Maximum2775
Range2224
Interquartile range (IQR)447

Descriptive statistics

Standard deviation346.20679
Coefficient of variation (CV)0.23773619
Kurtosis0.078018624
Mean1456.2646
Median Absolute Deviation (MAD)221
Skewness0.20538853
Sum13093275
Variance119859.14
MonotonicityNot monotonic
2025-11-22T10:04:58.800819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148824
 
0.3%
158022
 
0.2%
153921
 
0.2%
146720
 
0.2%
163819
 
0.2%
141818
 
0.2%
149018
 
0.2%
157017
 
0.2%
151117
 
0.2%
143517
 
0.2%
Other values (1593)8798
94.0%
(Missing)366
 
3.9%
ValueCountFrequency (%)
5511
< 0.1%
5591
< 0.1%
5611
< 0.1%
5791
< 0.1%
6011
< 0.1%
6021
< 0.1%
6051
< 0.1%
6211
< 0.1%
6371
< 0.1%
6401
< 0.1%
ValueCountFrequency (%)
27751
< 0.1%
27461
< 0.1%
26911
< 0.1%
26841
< 0.1%
26791
< 0.1%
26671
< 0.1%
26651
< 0.1%
26621
< 0.1%
26432
< 0.1%
26412
< 0.1%

PT08.S5(O3)
Real number (ℝ)

High correlation  Missing 

Distinct1743
Distinct (%)19.4%
Missing366
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean1022.9061
Minimum221
Maximum2523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size404.2 KiB
2025-11-22T10:04:59.312686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum221
5-th percentile461
Q1731.5
median963
Q31273.5
95-th percentile1761.5
Maximum2523
Range2302
Interquartile range (IQR)542

Descriptive statistics

Standard deviation398.48429
Coefficient of variation (CV)0.38956095
Kurtosis0.078612339
Mean1022.9061
Median Absolute Deviation (MAD)261
Skewness0.6278645
Sum9196949
Variance158789.73
MonotonicityNot monotonic
2025-11-22T10:04:59.744011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83620
 
0.2%
82520
 
0.2%
82619
 
0.2%
92618
 
0.2%
79917
 
0.2%
77717
 
0.2%
90516
 
0.2%
92316
 
0.2%
89116
 
0.2%
94916
 
0.2%
Other values (1733)8816
94.2%
(Missing)366
 
3.9%
ValueCountFrequency (%)
2211
< 0.1%
2251
< 0.1%
2271
< 0.1%
2321
< 0.1%
2521
< 0.1%
2531
< 0.1%
2571
< 0.1%
2612
< 0.1%
2621
< 0.1%
2631
< 0.1%
ValueCountFrequency (%)
25231
< 0.1%
25221
< 0.1%
25191
< 0.1%
25151
< 0.1%
24941
< 0.1%
24801
< 0.1%
24751
< 0.1%
24651
< 0.1%
24521
< 0.1%
24341
< 0.1%

T
Text

Distinct437
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size404.2 KiB
2025-11-22T10:05:00.945457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8233408
Min length3

Characters and Unicode

Total characters35775
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.3%

Sample

1st row13,6
2nd row13,3
3rd row11,9
4th row11,0
5th row11,2
ValueCountFrequency (%)
200366
 
3.9%
20,857
 
0.6%
21,354
 
0.6%
20,251
 
0.5%
13,851
 
0.5%
15,649
 
0.5%
12,349
 
0.5%
12,049
 
0.5%
16,348
 
0.5%
19,848
 
0.5%
Other values (417)8535
91.2%
2025-11-22T10:05:02.383598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,8991
25.1%
15366
15.0%
24858
13.6%
32911
 
8.1%
02485
 
6.9%
41952
 
5.5%
51855
 
5.2%
61798
 
5.0%
81788
 
5.0%
71729
 
4.8%
Other values (2)2042
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)35775
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,8991
25.1%
15366
15.0%
24858
13.6%
32911
 
8.1%
02485
 
6.9%
41952
 
5.5%
51855
 
5.2%
61798
 
5.0%
81788
 
5.0%
71729
 
4.8%
Other values (2)2042
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)35775
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,8991
25.1%
15366
15.0%
24858
13.6%
32911
 
8.1%
02485
 
6.9%
41952
 
5.5%
51855
 
5.2%
61798
 
5.0%
81788
 
5.0%
71729
 
4.8%
Other values (2)2042
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)35775
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,8991
25.1%
15366
15.0%
24858
13.6%
32911
 
8.1%
02485
 
6.9%
41952
 
5.5%
51855
 
5.2%
61798
 
5.0%
81788
 
5.0%
71729
 
4.8%
Other values (2)2042
 
5.7%

RH
Text

Distinct754
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size404.2 KiB
2025-11-22T10:05:03.486418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9992519
Min length3

Characters and Unicode

Total characters37421
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)0.3%

Sample

1st row48,9
2nd row47,7
3rd row54,0
4th row60,0
5th row59,6
ValueCountFrequency (%)
200366
 
3.9%
53,131
 
0.3%
57,930
 
0.3%
47,830
 
0.3%
60,827
 
0.3%
45,927
 
0.3%
49,826
 
0.3%
50,926
 
0.3%
43,426
 
0.3%
50,826
 
0.3%
Other values (744)8742
93.4%
2025-11-22T10:05:04.854628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,8991
24.0%
43578
 
9.6%
53499
 
9.4%
33316
 
8.9%
63257
 
8.7%
23000
 
8.0%
72630
 
7.0%
02569
 
6.9%
82194
 
5.9%
12140
 
5.7%
Other values (2)2247
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)37421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,8991
24.0%
43578
 
9.6%
53499
 
9.4%
33316
 
8.9%
63257
 
8.7%
23000
 
8.0%
72630
 
7.0%
02569
 
6.9%
82194
 
5.9%
12140
 
5.7%
Other values (2)2247
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)37421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,8991
24.0%
43578
 
9.6%
53499
 
9.4%
33316
 
8.9%
63257
 
8.7%
23000
 
8.0%
72630
 
7.0%
02569
 
6.9%
82194
 
5.9%
12140
 
5.7%
Other values (2)2247
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)37421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,8991
24.0%
43578
 
9.6%
53499
 
9.4%
33316
 
8.9%
63257
 
8.7%
23000
 
8.0%
72630
 
7.0%
02569
 
6.9%
82194
 
5.9%
12140
 
5.7%
Other values (2)2247
 
6.0%

AH
Text

Distinct6684
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size404.2 KiB
2025-11-22T10:05:05.707189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9217698
Min length4

Characters and Unicode

Total characters55410
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4872 ?
Unique (%)52.1%

Sample

1st row0,7578
2nd row0,7255
3rd row0,7502
4th row0,7867
5th row0,7888
ValueCountFrequency (%)
200366
 
3.9%
1,11996
 
0.1%
0,83946
 
0.1%
0,96846
 
0.1%
0,74876
 
0.1%
0,97226
 
0.1%
1,05945
 
0.1%
0,87365
 
0.1%
0,92715
 
0.1%
0,66865
 
0.1%
Other values (6674)8941
95.6%
2025-11-22T10:05:06.886807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,8991
16.2%
08868
16.0%
17744
14.0%
43831
6.9%
23821
6.9%
93804
6.9%
83659
6.6%
73645
6.6%
63578
 
6.5%
33552
 
6.4%
Other values (2)3917
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)55410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,8991
16.2%
08868
16.0%
17744
14.0%
43831
6.9%
23821
6.9%
93804
6.9%
83659
6.6%
73645
6.6%
63578
 
6.5%
33552
 
6.4%
Other values (2)3917
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)55410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,8991
16.2%
08868
16.0%
17744
14.0%
43831
6.9%
23821
6.9%
93804
6.9%
83659
6.6%
73645
6.6%
63578
 
6.5%
33552
 
6.4%
Other values (2)3917
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)55410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,8991
16.2%
08868
16.0%
17744
14.0%
43831
6.9%
23821
6.9%
93804
6.9%
83659
6.6%
73645
6.6%
63578
 
6.5%
33552
 
6.4%
Other values (2)3917
7.1%

Interactions

2025-11-22T10:04:43.549849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:21.035619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:24.674395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:27.295388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:30.056274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:33.770719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:37.408830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:40.991189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:43.880318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:21.550974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:25.048322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:27.604313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:30.386999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:34.334439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:37.838721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:41.343462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:44.171130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:22.051472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:25.396028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:27.938744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:30.725368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:34.826287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:38.335755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:41.695297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:44.474395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:22.546636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:25.743073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:28.283726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:31.157550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:35.483263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:38.801019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:42.028123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:44.788886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:22.990899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:26.044551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:28.638745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:31.569604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:35.854566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:39.375040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:42.360617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:45.098696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:23.383136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:26.375315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:28.970518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:32.428899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:36.230375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:39.794063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:42.647502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:45.391400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:23.760458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:26.689028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:29.391656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:32.850884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:36.610182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:40.186502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:42.945165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:45.651834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:24.271846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:27.027192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:29.725829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:33.340933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:37.019881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:40.607022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T10:04:43.209625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-22T10:05:07.168735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
NMHC(GT)NO2(GT)NOx(GT)PT08.S1(CO)PT08.S2(NMHC)PT08.S3(NOx)PT08.S4(NO2)PT08.S5(O3)Time
NMHC(GT)1.0000.8420.8840.8510.950-0.9500.8950.8040.116
NO2(GT)0.8421.0000.8350.6730.669-0.6890.1540.7090.137
NOx(GT)0.8840.8351.0000.7250.705-0.7900.1600.7930.090
PT08.S1(CO)0.8510.6730.7251.0000.889-0.8540.6460.8940.117
PT08.S2(NMHC)0.9500.6690.7050.8891.000-0.8500.7490.8740.139
PT08.S3(NOx)-0.950-0.689-0.790-0.854-0.8501.000-0.536-0.8620.095
PT08.S4(NO2)0.8950.1540.1600.6460.749-0.5361.0000.5610.088
PT08.S5(O3)0.8040.7090.7930.8940.874-0.8620.5611.0000.089
Time0.1160.1370.0900.1170.1390.0950.0880.0891.000

Missing values

2025-11-22T10:04:46.112810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-22T10:04:46.530942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-22T10:04:47.103406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAH
010/03/200418.00.002,61360.0150.011,91046.0166.01056.0113.01692.01268.013,648,90,7578
110/03/200419.00.0021292.0112.09,4955.0103.01174.092.01559.0972.013,347,70,7255
210/03/200420.00.002,21402.088.09,0939.0131.01140.0114.01555.01074.011,954,00,7502
310/03/200421.00.002,21376.080.09,2948.0172.01092.0122.01584.01203.011,060,00,7867
410/03/200422.00.001,61272.051.06,5836.0131.01205.0116.01490.01110.011,259,60,7888
510/03/200423.00.001,21197.038.04,7750.089.01337.096.01393.0949.011,259,20,7848
611/03/200400.00.001,21185.031.03,6690.062.01462.077.01333.0733.011,356,80,7603
711/03/200401.00.0011136.031.03,3672.062.01453.076.01333.0730.010,760,00,7702
811/03/200402.00.000,91094.024.02,3609.045.01579.060.01276.0620.010,759,70,7648
911/03/200403.00.000,61010.019.01,7561.0<NA>1705.0<NA>1235.0501.010,360,20,7517
DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAH
934704/04/200505.00.000,5888.0<NA>1,3528.077.01077.053.0987.0578.010,459,90,7550
934804/04/200506.00.001,11031.0<NA>4,4730.0182.0760.093.01129.0905.09,563,10,7531
934904/04/200507.00.004,01384.0<NA>17,41221.0594.0470.0155.01600.01457.09,761,90,7446
935004/04/200508.00.005,01446.0<NA>22,41362.0586.0415.0174.01777.01705.013,548,90,7553
935104/04/200509.00.003,91297.0<NA>13,61102.0523.0507.0187.01375.01583.018,236,30,7487
935204/04/200510.00.003,11314.0<NA>13,51101.0472.0539.0190.01374.01729.021,929,30,7568
935304/04/200511.00.002,41163.0<NA>11,41027.0353.0604.0179.01264.01269.024,323,70,7119
935404/04/200512.00.002,41142.0<NA>12,41063.0293.0603.0175.01241.01092.026,918,30,6406
935504/04/200513.00.002,11003.0<NA>9,5961.0235.0702.0156.01041.0770.028,313,50,5139
935604/04/200514.00.002,21071.0<NA>11,91047.0265.0654.0168.01129.0816.028,513,10,5028